Evaluating Natural Language Processing Applications Applied to Outbreak and Disease Surveillance

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Evaluating Natural Language Processing Applications Applied
to Outbreak and Disease Surveillance
Wendy W. Chapman, PhD
1
, John N. Dowling, MD, MS
1
, Oleg Ivanov,
MD, MPH, MS
1
, Per H. Gesteland, MD
2
, Robert T. Olszewski, PhD
3
Jer-
emy U. Espino
1
, MD, Michael M. Wagner, MD, PhD
1

1
RODS Laboratory, Center for Biomedical Informatics,
University of Pittsburgh, Pittsburgh, PA, USA.
2
Intermountain Healthcare, Salt Lake City, UT, USA
3
Carnegie Speech, Pittsburgh, PA, USA
Abstract
Much of the pre-existing electronic data that could be harnessed for early out-
break detection is in free-text format. Natural language processing (NLP) techniques may
be useful to biosurveillance by classifying and extracting information described in free-
text sources. In the Real-time Outbreak and Disease Surveillance laboratory we are de-
veloping and evaluating NLP techniques for surveillance of syndromic presentations and
specific findings or diseases potentially caused by bioterroristic or naturally-occurring
outbreaks. We have implemented a three-stage evaluation process to determine whether
NLP techniques are useful for outbreak detection.
First, we are evaluating the technical accuracy of the NLP techniques to answer
the question “How well can we classify, extract, or encode relevant information from
text?” Second, we are evaluating the diagnostic accuracy of the techniques to answer the
question “How well can we diagnose patients of interest using the NLP techniques?”
Third, we are evaluating the outcome efficacy of the techniques to answer the question
“How well can we detect outbreaks with an NLP-based biosurveillance system?” We
give examples from our research for all three levels of evaluation and conclude with sug-
gestions for determining whether NLP is feasible for outbreak and disease surveillance.
Introduction and Background
The appearance of new infectious diseases (e.g., the outbreak of Severe Acute
Respiratory Syndrome (SARS) in Asia and Toronto), the reemergence of old infectious
diseases (e.g., tuberculosis outbreaks), and the deliberate introduction of infectious dis-
eases through bioterrorism (e.g., October 2001 anthrax attacks) demonstrate the need for
surveillance of infectious disease [1]. The United States has not been prepared to deal
with biological attacks [2], and biodefense has quickly become a national priority [3]. In
response to the need for better biodefense, several research groups have developed elec-
tronic surveillance systems [4-13] that monitor a variety of different sources of data in-
cluding over-the-counter drug sales [14, 15], web-based physician entry of reports [16],
911 calls [17], consumer health hotline telephone calls [18, 19], and ambulatory care visit
records [20-24]. Many of the systems monitor pre-existing electronic ED data [25] that
typically include date of admission, sex, age, address, coded discharge diagnosis [22, 23,
26], and free-text triage chief complaint [21, 27-30]. Detection algorithms count the
number of occurrences of a variable or a combination of variables in a given spatial loca-
tion over a given time period to look for anomalous patterns [21, 31-33]. If the algorithms
detect a significant increase in a given variable, such as the number of patients with a
gastrointestinal illness, the detection algorithms alarm relevant medical and public health
officials of a possible outbreak.
The Real-time Outbreak and Disease Surveillance (RODS) system [34] is a
biosurveillance system adherent to the CDC’s NEDSS standards [35] that was developed
in 1999 at the University of Pittsburgh and is currently deployed in four states, including
Pennsylvania, Utah, New Jersey, and Ohio. For over 100 hospitals in the four states,
RODS collects real-time admission data, including age, sex, zip code, and triage chief
complaint. Time-series detection algorithms are applied to the information in the data-
base, and the counts of patients with seven types of syndromic presentations are shown in
graphical form on the user interface, shown in Figure 1. The interface also includes a
geographic information system that shows counts of syndromic presentations by zip code.
If the actual number of patients presenting with gastrointestinal complaints, for instance,
exceeds the number expected in a given geographical location over a given time period,
RODS’ notification subsystem sends an electronic alarm to a team of researchers and
public health physicians for possible investigation. RODS software is currently open
source [36] and is available for free download at www.health.pitt.edu/rods/sw
.
Input variables for the detection algorithms in RODS and other biosurveillance
systems must be coded data, i.e., data stored in a format that can be interpreted by a com-
puter. For example, a biosurveillance system may monitor the number of patients with
Pneumonia, which may indicate a possible outbreak of Influenza, SARS, or inhalational
Anthrax. Researchers in medical informatics have developed electronic diagnostic sys-
tems integrating multiple sources of data from a patient’s medical record to generate a
probability that a patient has Pneumonia [37-39]. The variables required to determine the
probability of Pneumonia include age and risk factors, vital signs, symptoms and physical
findings, laboratory results, blood gas levels, and chest radiograph results. Values for
Figure 1. Interface for Real-time Outbreak and Disease (RODS) system, showing syndromic
classifications over a one-week period for all admissions in the specificed jurisdiction.

some of the variables are stored in hospital information systems in coded format; how-
ever, variables involving a patient’s symptoms and physical findings, such as cough or
adventitious respiratory sounds, and the results of a chest radiograph are usually stored as
dictated reports in uncoded, free-text format. To use these variables for computerized
decision support, the variables must be encoded from the textual reports.
A physician reading the reports could easily determine the correct values for the
variables. However, paying physicians to encode reports is impractical. A more feasible
solution is applying natural language processing (NLP) techniques to convert the free-
text data into an encoded representation that can be used for later inference [40].
Over the last few decades the medical informatics community has actively ap-
plied NLP techniques to the medical domain [41, 42]. The Linguistic String Project de-
veloped one of the first medical NLP systems that included comprehensive semantic and
syntactic knowledge [43-49] that has also been ported to French and German [50-55].
Columbia Presbyterian Medical Center has evaluated and deployed a system called
MedLEE [56-60] that extracts clinical information from radiology reports, discharge
summaries, visit notes, electrocardiography, echocardiography, and pathology notes.
MedLEE has been shown to be as accurate as physicians at extracting clinical concepts
from chest radiograph reports [61, 62] and has been evaluated for a variety of applica-
tions including detecting patients with suspected tuberculosis [63-65], identifying find-
ings suspicious for breast cancer [66], stroke [67], and community acquired Pneumonia
[68], and deriving co morbidities from text [69].
Other medical informatics research groups have also created and evaluated NLP
systems for extracting clinical information from medical texts and have shown them to be
accurate in limited domains [70-86]. NLP techniques have been used for a variety of ap-
plications including quality assessment in radiology [87, 88], identification of structures
in radiology images [89, 90], facilitation of structured reporting [72, 91] and order entry
[92, 93], and encoding variables required by automated decision support systems such as
guidelines [94], diagnostic systems [95], and antibiotic therapy alarms [96].
NLP has only recently been applied to the domain of outbreak and disease sur-
veillance, and most of the research has focused on processing free-text chief complaints
recorded in the emergency department [97-102]. We have applied NLP techniques to
chief complaints, ED reports, and chest radiograph reports in order to acquire coded vari-
ables that may be useful in outbreak detection. To quantify the value of NLP in the do-
main of biosurveillance, we have adapted a hierarchical model of technology assessment
from the domain of medical imaging, described by Thornbury and Fryback [103]. First,
we have evaluated the technical accuracy of our NLP techniques to answer the question
“How well can we classify, extract, or encode relevant information from text?” Second,
we have evaluated the diagnostic accuracy of the techniques to answer the question “How
well can we diagnose patients of interest using the NLP techniques?” Third, we have
evaluated the outcome efficacy of the techniques to answer the question “How well can
we detect outbreaks with an NLP-based biosurveillance system?”
In the Methods section we describe the three levels of evaluation, using the hy-
pothetical example of Pneumonia surveillance as an example. We briefly describe studies
we have performed to evaluate NLP technologies for all three levels of evaluation and
provide references for details about the studies. In the Results section we provide results
from our research for the three levels of evaluation. In the Discussion section, we discuss
implications of our findings and suggest three points to consider when appraising the
feasibility of applying NLP to the domain of outbreak detection.
Methods
Research in applying NLP technologies to problems of interest in the medical
and public health fields is still in the early stages, and NLP systems are only beginning to
become accurate enough to be applied to real-world problems. Only a handful of studies
have evaluated the impact of NLP technology in healthcare; even fewer have examined
its performance in the field of public health. Like other studies involving automated tech-
nology, studies in NLP begin by validating the technical accuracy of the technology, and
the majority of evaluations of NLP technology in the biomedical domain have focused on
this phase of evaluation. Once technical accuracy has been validated, the diagnostic accu-
racy of the technology can be assessed. In outbreak and disease surveillance, diagnostic
accuracy refers to the technology’s ability to diagnose or detect specific cases of interest.
Finally, summative evaluations addressing outcome efficacy, showing that an NLP-based
system can impact the healthcare of a population, can be performed. In the biosurveil-
lance domain, a study validating outcome efficacy would show that an NLP-based detec-
tion system can detect epidemics. Figure 2 shows how the three levels of evaluation
relate to one another, using a diagnostic system for Pneumonia as an example.
The type of evaluation being performed affects both the appropriate reference
standard required for calculating performance metrics and the extent to which the results
inform us about NLP’s contribution to biosurveillance. Evaluations of technical accuracy
quantify how well the NLP technology determines the values of relevant variables from
text. Therefore, the reference standard must perform the same task and be generated from
the same text as the NLP application. Good performance on a technical accuracy evalua-
tion indicates that the NLP application performs the task it was designed to perform.
Evaluations of diagnostic accuracy quantify how well assigning values to the variables
Risk Factors
Vital Signs
Respiratory
Findin
g
s
Laboratory
Results
Blood Gas
CXR Re-
sults
Probability
Pneumonia
Medical Record
NLP
System
Technical
Accuracy
Respiratory Findings
Cough: yes/no
Dyspnea: yes/no
Respiratory sounds: yes/no
Positive CXR Result: yes/no
Diagnostic
Accuracy
Outcome
Efficacy
Number of patients
with Pneumonia
Figure 2. Relationship between the three levels of evaluation for biosurveillance. Evaluations of
technical accuracy quantify how well variables and their values are automatically encoded from
text. Evaluations of diagnostic accuracy quantify the ability to accurately diagnose a single
patient from the variables encoded from text, which may or may not be combined with other
variables. Evaluations in outcome efficacy quantify whether the variable being monitored by
detection algorithms can detect outbreaks.

contributes to accurately diagnosing individual patients. The reference standard for diag-
nostic accuracy evaluations is the actual diagnosis of the patient. Good performance on a
diagnostic accuracy evaluation indicates that the NLP application can contribute to diag-
nosing patients. Evaluations of outcome efficacy quantify how well assigning the values
to the variables contributes to detection of an outbreak. The reference standard is the
presence of an outbreak. Good performance on an outcome efficacy evaluation indicates
that the NLP application can contribute to outbreak detection.
Generating a reference standard for evaluation of automated expert systems is
difficult, because deciding what the correct response from the system should be and what
level of performance is good enough are challenges. Evaluation of biosurveillance sys-
tems is sometimes even more challenging for several reasons. First, some of the variables
being evaluated have not previously been defined. For example, syndromic definitions,
such as gastrointestinal and respiratory, have only recently been explicitly defined for the
purpose of disease and outbreak surveillance and have not been externally validated as
other case definitions have (e.g., Pneumonia or Influenza). Second, many of the diseases
surveillance systems are trying to detect rarely, if ever, occur. For instance, in hospitals in
the United States, patients with hemorrhagic or botulinic syndrome and patients with An-
thrax or West Nile Virus are rarely seen. Third, the existing reference standard may not
be better than the system being tested. For example, the reference standard for existence
of an outbreak is currently lab-verified diagnoses and physician reporting of communica-
ble diseases, which may be available later than outbreaks detected by an automated
surveillance system and may provide incomplete representation of the extent of the
outbreak. Because of the challenges involved in generating a reference standard, evalua-
tions of NLP technology in biosurveillance have begun by answering some of the simpler
questions about NLP’s contribution to outbreak detection.
Technical Accuracy of NLP in Biosurveillance

The first phase of evaluation for an NLP application should be one of technical
accuracy. The question being addressed when measuring the technical accuracy of an
NLP application for the domain of outbreak and disease surveillance is How well does the
NLP application determine the values to the variables of interest from text? For a Pneu-
monia detector, examples of technical accuracy evaluations include how well the NLP
application can determine whether a textual document describes cough, shortness of
breath, adventitious respiratory sounds, or radiological evidence of Pneumonia.
We have evaluated the technical accuracy of our ability to classify and encode
variables from chief complaints, chest radiograph reports, and emergency department
(ED) reports and provide outcome measures for the following NLP tasks:

(1) Encoding diarrhea, vomiting [104], and fever [105] from chief complaints. We
calculated the sensitivity, specificity, positive predictive value, and negative predic-
tive value of our ability to use keyword matching to identify chief complaints that
indicate diarrhea (e.g., diarrhea, n/v/d, loose stools, etc.), vomiting (e.g., vomiting,
vomitting, throwing up, etc.), and fever (e.g., fever, febrile, temp). The reference
standard was a physician reading the same chief complaints and determining
whether the complaints described any of the variables.
(2) Classifying chief complaints into syndromic categories. Syndromic surveillance
is the practice of monitoring any pattern preceding diagnosis for a signal with suffi-
cient probability of an outbreak to warrant further public health response [106].
Grouping cases into syndromes (e.g., respiratory syndrome) rather than into specific
diagnoses (e.g., Pneumonia) can provide earlier evidence of infection, because many
diseases in their early phase have overlapping symptoms that may not initially alarm
clinicians [107-112]. We developed and evaluated two syndromic classifiers that
can classify patients into eight possible syndromic categories based on the patients’
chief complaints. For example, a patient with the chief complaint “short of breath”
should be classified as respiratory, and a patient with the chief complaint “n/v/d abd
pain” should be classified as gastrointestinal. The first classifier is a naïve Bayesian
classifier called CoCo [97]. The second classifier is an adaptation of an existing
NLP application called MPLUS [113, 114]. We measured the area under the ROC
curve (AUC) to determine how accurately the classifiers assigned syndromic catego-
ries based on physician gold standard classification of the same chief complaints.
(3) Classifying chest radiograph reports consistent with acute bacterial Pneumo-
nia. In previous studies using medical records from LDS Hospital in Salt Lake City,
Utah, we showed that an NLP application called SymText performed similarly to
physicians at determining whether chest radiograph reports were consistent with
acute bacterial Pneumonia [96]. With this same goal in mind for chest radiograph
reports in Pittsburgh, we created a keyword search that accounted for negation and
applied it to reports from the University of Pittsburgh Medical Center (UPMC). We
compared the keyword search’s classification of Pneumonia against that of a physi-
cian reading the same chest radiograph reports and calculated sensitivity, specificity,
positive predictive value (PVP), and negative predictive value (NPV).
(4) Classifying chest radiograph reports describing mediastinal findings consistent
with anthrax [115]. We used the IPS system [116] [117] to classify 79,032 chest
radiograph reports based on whether the report described mediastinal lymphade-
nopathy or widening. We compared the IPS classifications against the baseline of a
simple keyword search classifier and calculated sensitivity, specificity, PPV, and
NPV for the two classifiers. The reference standard was generated from majority
vote of three physicians reading the same reports.
(5) Indexing respiratory-related findings from Emergency Department (ED) re-
ports [118]. We applied an existing indexing application called MetaMap [119] to
28 UPMC ED reports to index individual instances of 71 respiratory-related findings
and diseases, such as cough, shortness of breath, pulmonary mass, asthma, Pneumo-
nia, etc. Using a physician as the reference standard, we calculated sensitivity and
PPV for MetaMap’s ability to identify every instance of the 71 findings within the
ED reports.

Diagnostic Accuracy of NLP for Biosurveillance
The question being addressed when measuring the diagnostic accuracy of an
NLP application for the domain of outbreak and disease surveillance is How well does the
NLP application diagnose patients from textual data? For a Pneumonia detector, an
evaluation of diagnostic accuracy would determine how well the Pneumonia detector
determined whether or not study patients had Pneumonia when compared against a refer-
ence standard diagnosis. The reference standard for diagnostic accuracy depends on the
finding, syndrome, or disease being diagnosed and may comprise review of textual pa-
tient reports or complete medical records, results of laboratory tests, autopsy results, etc.
The NLP application extracts the values of relevant variables from text. Depend-
ing on the analysis, the NLP output could be the sole contributor in the system diagnosis,
or the variables extracted with NLP may be combined in an expert system like a Pneu-
monia detector to generate the system diagnoses. Coded variables from other sources,
such as microbiology test results or coded admit diagnoses, may also be integrated by the

expert system in generating system diagnoses. Diagnostic accuracy performance is calcu-
lated by comparing the diagnoses generated by the reference standard against those gen-
erated by the system.
Because evaluations of diagnostic accuracy assess the NLP application’s accu-
racy in relation to the actual clinical state of the patient, diagnostic accuracy evaluations
address not only the performance of the NLP technology but also the adequacy of the
input text in representing the patient’s state. Below we describe examples of diagnostic
accuracy evaluations from our research that demonstrate this point.
(1) Classifying patients into syndromic categories based on their chief complaints.
We have performed several studies to determine how well we can classify patients
into syndromic categories using only their chief complaints. The difference between
technical and diagnostic accuracy evaluations of chief complaint classification is the
reference standard: the reference standard for technical accuracy was a physician’s
classification of the patient from the chief complaint; the reference standard for diag-
nostic accuracy was diagnosis of the patient either by physician review of the pa-
tient’s chart or by discharge diagnosis. It is possible to classify a patient correctly
according to the chief complaint string but to misclassify the patient based on their
actual diagnosis. For example, a patient with a chief complaint of “abdominal pain”
may be correctly classified into the gastrointestinal category according to a technical
accuracy evaluation. However, the patient’s actual diagnosis may be Pneumonia,
which is a respiratory syndrome, resulting in an incorrect classification in a diagnostic
accuracy evaluation.
We summarized the results of four diagnostic accuracy evaluations of CoCo’s ability
to classify patients into one of up to seven different syndromes and report the range of
sensitivities and specificities for the relevant studies. The first study [99] used physi-
cian review of ED reports as the reference standard for identifying patients with
acute, infectious gastrointestinal syndrome and compared reference standard classifi-
cations against those generated by CoCo’s syndromic classification from chief com-
plaints. This study evaluated 585 patients, with 14 positive cases. The second study,
based on data described in [120], evaluated CoCo’s ability to classify patients into
acute, lower respiratory syndrome and compared CoCo’s classifications against phy-
sician classifications from ED reports [104]. The study evaluated 620 patients, with
30 positive cases. The third study [121] compared CoCo’s classifications of patients
seen in urgent care facilities in Utah during the Winter Olympic games against two
different reference standards: the first reference standard was ICD-9 primary dis-
charge diagnosis; the second was manual classification of patients by Utah Depart-
ment of Health reviewers, who classified the patients into syndromic categories by
review of the chief complaint and the patients’ full charts. The Gesteland study exam-
ined CoCo’s ability to classify 30,094 patients into five syndromic categories, includ-
ing respiratory, gastrointestinal, neurological, rash, and botulinic, and the number of
positive patients ranged from 12 to 2,957, depending on the syndrome. The fourth
study [122] evaluated CoCo’s classification ability for seven syndromes over a thir-
teen-year period at UPMC, using primary ICD-9 discharge diagnoses as the reference
standard. In this study we evaluated CoCo’s classification performance on seven syn-
dromic categories (respiratory, gastrointestinal, neurological, rash, botulinic, constitu-
tional, and hemorrhagic) for 527,228 patients. Positive cases ranged from 1,961 to
34,916, depending on the syndrome.
(2) Diagnosing Fever from Chief Complaints [105]. We calculated the sensitivity and
specificity with which we could diagnose patients who were febrile based on key-
words in their chief complaints (fever, febrile, temp). The reference standard was
physician determination of fever based on information described in the ED report.

(3) Diagnosing Fever from ED Reports [105]. We developed an NLP application to
detect fever from ED reports. The algorithm accounted for negation (e.g., not febrile)
with an algorithm called NegEx [123] and for hypothetical findings (e.g., return for
fever). We calculated the sensitivity and specificity of the application at diagnosing
patients with fever when compared against a reference standard of physician judg-
ment from the same ED report.
Outcome Efficacy of NLP for Biosurveillance
The question being addressed when measuring the outcome efficacy of an NLP
application for the domain of outbreak and disease surveillance is How well does the NLP
application contribute to detection of an outbreak? Two important aspects of evaluating
outcome efficacy are predictive performance (i.e., how well the system detects outbreaks)
and timeliness (i.e., how soon the system detects an outbreak). For a Pneumonia detector,
an outcome efficacy evaluation would measure whether the Pneumonia diagnostic system
detected a pneumonic outbreak or whether the system could have detected the outbreak
sooner than standard detection techniques.
The first requirement for an outcome efficacy study in outbreak detection is ref-
erence standard identification of an outbreak. Outbreaks of respiratory and GI illnesses,
such as Influenza, Pneumonia, and Gastroenteritis, occur yearly throughout the country.
Outbreaks of other infectious or otherwise concerning diseases, such as Anthrax, West
Nile Virus, Hemorrhagic Fever, or SARS, rarely occur in the United States. Once an out-
break is identified, the next requirement for an outcome efficacy evaluation is having
access to textual data for an adequate sample of patients living in the geographical area of
the outbreak. For instance, if we wanted to evaluate how well our Pneumonia diagnostic
system could have detected the 2003 SARS outbreak in Hong Kong had it been deployed
at the time, we would need to apply our NLP techniques to relevant textual patient reports
generated in Hong Kong in order to extract the values needed for the variables in the
Pneumonia detector. Access to personal clinical documents is not easily obtained for re-
search purposes and requires an extraordinary amount of cooperation and trust among
researchers and research institutions, hospitals, and local, state, and even federal govern-
ments. The two requirements for evaluation of outcome efficacy are not easily attained;
therefore, evaluating the contribution of NLP to outcome detection is still in its infant
stages.
Studies of technical and diagnostic accuracy of chief complaint syndromic clas-
sification were described above, and we have performed one evaluation of the outcome
efficacy [100] of chief complaints in detecting pediatric outbreaks. The difference be-
tween the previous chief complaint classification evaluations and the outcome efficacy
evaluation is the reference standard: the reference standard for the outcome efficacy study
was seasonal outbreaks of respiratory and gastrointestinal illnesses. The outcome effi-
cacy evaluation used ICD-9 discharge diagnoses to define retrospective outbreaks of pe-
diatric respiratory and gastrointestinal syndromes using over a five year period (1998-
2001) in four contiguous counties in Utah. Sensitivity and specificity of outbreak detec-
tion was reported, along with measures of timeliness of detection.

Results
Technical Accuracy of NLP in Biosurveillance
(1) Encoding diarrhea, vomiting [104], and fever [105] from chief complaints. Us-
ing keyword matching of chief complaint strings, we were able to identify chief
complaints describing diarrhea, vomiting, and fever with 100% accuracy. Every
chief complaint considered positive for any of the three variables by the reference
standard physician was identified with the keyword searches, and the keyword
searches generated no false positives.
(2) Classifying chief complaints into syndromic categories. We developed and evalu-
ated two syndromic classifiers at classifying chief complaints into syndromic cate-
gories and show results for the two studies in Figure 3. MPLUS performed with
AUC’s between 0.95 and 1.0 [114]; CoCo performed with AUC’s between 0.78 and
0.97[97].
(3) Classifying chest radiograph reports consistent with acute bacterial Pneumo-
nia. A keyword search with simple negation processing applied to 200 chest radio-
graph reports identified reports consistent with acute bacterial pneumonia with a
sensitivity of 85%, specificity of 96%, PPV of 83%, and NPV of 96%.
(4) Classifying chest radiograph reports describing mediastinal findings consistent
with anthrax [115]. We compared a simple keyword search against a naïve Bayes-
ian statistical query created using the IPS system on 79,032 chest radiograph reports,
of which 1,729 were positive according to the reference standard. Table 1 shows re-
sults for both classifiers. We performed a secondary evaluation on the reports by
modifying the IPS classifier based on a review of the false negative reports. Sensi-
tivity of the IPS classifier increased to 85.6%, and PPV dropped to 40.9%. Because
we used the test set to refine the classifier, results of the secondary evaluation are
higher than would be expected on a new test set.

0
0.2
0.4
0.6
0.8
1
Botul Const GI Hem Neurol Rash Resp
Syndrome
AUC
MPLUS
CoCo

Figure 3. Area under the ROC curve for classifying chief complaint strings
into syndromic categories (Botulinic (Botul), Constitutional (Const), Gas-
trointestinal (GI), Hemorrhagic (Hem), Neurological (Neurol), Rash, and
Respiratory (Resp)). MPLUS was tested on a set of 800 chief complaints;
CoCo used cross-validation testing on a set of 28,990 chief complaints.
There were no botulinic cases in MPLUS’ test set.
Table 1. Performance of three classifiers at identifying chest radiograph reports describing medi-
astinal findings consistent with anthrax. Numbers shown are percentages.
Keyword Search IPS Model Refined IPS Model
Sensitivity 43.0 35.1 85.6
Specificity 99.9 99.9 98.8
PPV 96.5 96.5 40.8
NPV 98.6 98.6 99.9

(5) Indexing respiratory-related findings from ED reports [118]. MetaMap indexed
respiratory-related findings from 28 ED reports with a sensitivity of 70% and a PPV
of 55%. Errors were in large part due to the need to model contextual information in
an ED report, such as whether a finding occurred in the past history or at the current
visit, and to mistakes from the single physician reference standard.
Diagnostic Accuracy of NLP for Biosurveillance
(1) Classifying patients into syndromic categories based on their chief complaints.
Table 2 summarizes our results from several evaluations in which patients are classi-
fied into syndromic categories by CoCo based on their chief complaints. We show a
range of the lowest and highest sensitivity and specificity for the evaluations. Over-
all, about two-thirds of the patients with relevant syndromic presentations were de-
tected by CoCo, with specificities ranging from 90-99%.

Table 2. Diagnostic accuracy evaluations of CoCo’s syndromic classifications. Sensitivities and
specificities are shown in percentages.

Syndrome
Number
Studies
Reference
Standard
Range
of Sen-
sitivity
Range of
Specificity
Respiratory
[104, 121, 122]
5
ICD-9 discharge diagnosis,
human chart review
60-77 90-94
Gastrointestinal
[99, 121, 122]
4
ICD-9 discharge diagnosis,
human chart review
63-74 90-96
Neurological
[121, 122]
3
ICD-9 discharge diagnosis,
human chart review
68-72 93-95
Rash [121, 122] 3
ICD-9 discharge diagnosis,
human chart review
47-60 99
Botulinic [121, 122] 3
ICD-9 discharge diagnosis,
human chart review
17-30 99
Hemorrhagic [122] 1 ICD-9 discharge diagnosis 75 98
Constitutional [122] 1 ICD-9 discharge diagnosis 46 97

(2) Diagnosing Fever from Chief Complaints [105]. Using a keyword search on chief
complaints, we were able to classify patients according to whether or not the patient
actually had a fever with 61% sensitivity and 100% specificity.


(3) Diagnosing Fever from ED Reports [105]. Applying NLP tools to ED reports, we
were able to detect patients who were febrile with a sensitivity of 98% and a specific-
ity of 89%.
Outcome Efficacy of NLP for Biosurveillance
We evaluated the ability of syndromic classifications from chief complaints to
detect seasonal outbreaks. Figure 4 shows time-series plots from [100] of pediatric chief
complaint syndromic classifications against ICD-9 discharge diagnoses for admissions of
patients with (a) infectious lower respiratory tract illness due to Pneumonia, Influenza,
and Bronchiolitis and (b) infectious gastrointestinal illness due to Rotavirus or other
causes of pediatric Gastroenteritis.
Sensitivity and specificity of outbreak detection for respiratory and gastrointes-
tinal outbreaks was 100%. Outbreaks were detected from chief complaints an average of
10.3 days earlier for respiratory and 29 days earlier for gastrointestinal.

Discussion
The first step to evaluating a new technology is to measure its technical accu-
racy. In the domain of biosurveillance, we have applied different types of NLP tech-
niques to chief complaints, chest radiograph reports, and ED reports and have extracted
individual findings and classified documents into syndromic or other disease categories.

(a)
(b)

Figure 4. Time series plot of chief complaint syndromic classifications against ICD-9 discharge
diagnoses for (a) admissions of patients with Pneumonia, Influenza, and Bronchiolitis and (b)
admissions of patients with Rotavirus and other causes of Gastroenteritis.
Our evaluations of technical accuracy suggest that NLP techniques are accurate at identi-
fying single findings from chief complaints. For example, we identified chief complaints
describing vomiting, diarrhea, and fever with perfect accuracy. NLP techniques are also
very good at classifying chief complaints into syndromic categories and do quite well at
classifying chest radiograph reports based on whether the report describes findings con-
sistent with Pneumonia or inhalational Anthrax. Identifying multiple findings from tex-
tual reports, such as ED reports, that entail temporality, describe multiple subjects (e.g.,
patient, physician, other caregivers, and family members), and address findings from
multiple anatomic locations is a much more complex task that requires more than the
phrase-based or sentence-based techniques we applied in our other studies. To accurately
extract multiple findings from ED reports, we need to apply more sophisticated NLP
techniques that model not only local information about the finding, but global informa-
tion about the report as a whole.
The next step in understanding a technology’s accuracy in biosurveillance is to
evaluate its diagnostic accuracy. Evaluations of diagnostic accuracy compare diagnoses
made by the NLP-based system against reference standard diagnoses and convey infor-
mation about both the accuracy of the NLP system and the quality of the input data. We
have shown that CoCo’s chief complaint classification can detect about two-thirds of the
patients a syndromic surveillance system would ideally detect. This finding has two im-
plications: First, CoCo is accurate enough to detect the majority of relevant patients and
second, the majority of the chief complaints of relevant patients reflect the actual syn-
dromic presentation of the patient.
Because evaluations of diagnostic accuracy evaluate not only the NLP applica-
tion’s performance but also the quality of the input data, good technical accuracy does
not ensure good diagnostic accuracy. Our studies of fever detection illustrate this point.
An evaluation of technical accuracy for identifying chief complaints describing fever
showed 100% sensitivity and specificity. However, the fact that we could perfectly iden-
tify chief complaints describing fever did not mean we could perfectly identify patients
with a fever: when the evaluation was one of diagnostic accuracy so that the reference
standard was the patient’s actual diagnosis instead of the words in the chief complaint
string, our technique maintained 100% specificity, but sensitivity dropped to 61%. Impli-
cations of diagnostic accuracy evaluations apply not only to the NLP technique, but also
to the data source. Our studies suggest that, in spite of the fact that chief complaints are
entered before the patient is examined by a physician and comprise only short phrases,
chief complaints are a fairly rich source of information for biosurveillance. The diagnos-
tic accuracy evaluation for fever detection also suggests that to increase sensitivity (e.g.,
from 61% for chief complaints to 98%), we need to look for information in more detailed
clinical records, such as the ED report.
Outbreak efficacy is the most difficult evaluation to perform, requiring collabo-
ration of multiple entities in order to access relevant clinical data and requiring defined
outbreaks. Our single study in outbreak detection has reinforced the belief gained from
technical and diagnostic accuracy studies that syndromic chief complaint classification
can be a powerful source for outbreak detection, at least for respiratory and gastrointesti-
nal outbreaks.

Feasibility of Using NLP for Biosurveillance
Natural language processing techniques are far from perfect. However, the question is not
whether the techniques perform perfectly but whether the performance is good enough to
contribute to disease and outbreak detection.
We suggest three questions to consider when deciding whether application of
NLP techniques to textual data is feasible for disease and outbreak detection: (1) How

complex is the text? The simple phrases in chief complaints are much simpler to under-
stand than complex discourses contained in ED reports. Textual data that require tempo-
ral modeling and other more sophisticated techniques to identify values for the variables
of interest will be more challenging to process and will be more prone to error; (2) What
is the goal of the NLP technique? If the goal is to understand all temporal, anatomic, and
diagnostic relations described in the text as well as a physician could, you may be in for a
lifetime of work. Extraction of a single variable, such as fever, or encoding temporal,
anatomic, and diagnostic relations for a finite set of findings, such as all respiratory find-
ings, is more feasible; (3) Can the detection algorithms that will use the variables ex-
tracted with NLP handle noise? Detecting small outbreaks requires more accuracy in the
input variables. As an extreme example, automated detection of an outbreak would fail if
a single case would be considered a threatening outbreak, which is true of diseases such
as inhalational anthrax, and the NLP-based expert system did not correctly detect that
case. However, in detecting an outbreak in respiratory syndrome, for example, if the
NLP-based expert system only detected two-thirds of the true cases, there may still be
enough patients to detect a moderate to large-sized outbreak. In addition, the consistent
stream of false positive cases identified by the NLP-based expert system would comprise
a noisy baseline that may not prevent the algorithm from detecting a significant increase
in respiratory cases but would require a larger increase to detect the outbreak. Considera-
tion of these three questions can help determine the feasibility of using NLP for outbreak
and disease surveillance.
Conclusion
NLP techniques can be applied to determine the values of predefined variables
that may be useful in detecting outbreaks. The complexity of the textual data being proc-
essed and the nature of the variables being used for surveillance determine the feasibility
of applying NLP techniques to the problem. Because many of the variables helpful in
biosurveillance do not require complete understanding of the text, NLP techniques may
successfully extract variables useful for outbreak detection. In fact, our research measur-
ing the technical accuracy, diagnostic accuracy, and outcome efficacy of NLP techniques
demonstrates the utility of NLP techniques for a few applications in this new field. More
research in NLP techniques and more evaluation studies of the effectiveness of NLP will
not only increase our understanding of how to extract information from text but will also
help us continue to learn what types of data provide the most timely and accurate infor-
mation for detecting outbreaks.
Acknowledgments
This work was funded by NLM training grant T15 LM07059, CDC U90/CCU318753-02,
DARPA F30602-01-2-0550, AHRQ 1 UO1 HS014683-01, and PA Department of Health
ME-01-737.
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M. Wagner. Creating A Text Classifier to Detect Radiology Reports Describing
Mediastinal Findings Associated with Inhalational Anthrax and Other Disorders.
J Am Med Inform Assoc. (2003) 10:494-503.
116. G. F. Cooper, B. G. Buchanan, M. Kayaalp, M. Saul, J. K. Vries. Using com-
puter modeling to help identify patient subgroups in clinical data repositories.
Proc AMIA Symp. (1998):180-4.
117. J. M. Aronis, G. F. Cooper, M. Kayaalp, B. G. Buchanan. Identifying patient
subgroups with simple Bayes'. Proc AMIA Symp. (1999):658-62.
118. W. W. Chapman, M. Fiszman, J. N. Dowling, B. E. Chapman, T. C. Rindflesch.
Identifying respiratory features from emergency department reports for biosur-
veillance with MetaMap. Medinfo. (2004):(in press).
119. A. R. Aronson. Effective mapping of biomedical text to the UMLS Metathesau-
rus: the MetaMap program. Proc AMIA Symp. (2001):17-21.
120. J. U. Espino, M. M. Wagner. Accuracy of ICD-9-coded chief complaints and
diagnoses for the detection of acute respiratory illness. Proc AMIA Symp.
(2001):164-8.
121. P. H. Gesteland, M. M. Wagner, R. M. Gardner, W. W. Chapman, R. T. Rolfs,
M. B. Mundorff, et al. Surveillance of syndromes during the Salt Lake 2002
Winter Olympic Games: an evaluation of a naive bayes chief complaint coder.
(2004):(in preparation).
122. W. W. Chapman, J. N. Dowling, M. M. Wagner. Syndromic Case Classification
from Chief Complaints: a Retrospective Analysis of 527,228 Patients. Technical
Report, CBMI Report Series. (2004).
123. W. W. Chapman, W. Bridewell, P. Hanbury, G. F. Cooper, B. G. Buchanan. A
simple algorithm for identifying negated findings and diseases in discharge
summaries. J Biomed Inform. (2001) 34:301-10.